Publications

Abstract not provided.

Abstract not provided.

This paper presents a Generalized Logistic (gLG) distribution as a unified model for Log-domain synthetic aperture Radar (SAR) data. This model stems from a special case of the G-distribution known as the G{sup 0}-distribution. The G-distribution arises from a multiplicative SAR model and has the classical K-distribution as another special case. The G{sup 0}-distribution, however, can model extremely heterogeneous clutter regions that the k-distribution cannot model. This flexibility is preserved in the unified gLG model, which is capable of modeling non-polarimetric SAR returns from clutter as well as man-made objects. Histograms of these two types of SAR returns have opposite skewness. The flexibility of the gLG model lies in its shape and shift parameters. The shape parameter describes the differing skewness between target and clutter data while the shift parameter compensates for movements in the mean as the shape parameter changes. A Maximum Likelihood (ML) estimate of the shape parameter gives an optimal measure of the skewness of the SAR data. This measure provides a basis for an optimal target detection algorithm.